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arXiv · 2506.11678

Pose Matters: Evaluating Vision Transformers and CNNs for Human Action Recognition on Small COCO Subsets

MingZe Tang·Madiha Kazi
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Abstract

This study explores human action recognition using a three-class subset of the COCO image corpus, benchmarking models from simple fully connected networks to transformer architectures. The binary Vision Transformer (ViT) achieved 90% mean test accuracy, significantly exceeding multiclass classifiers such as convolutional networks (approximately 35%) and CLIP-based models (approximately 62-64%). A one-way ANOVA (F = 61.37, p < 0.001) confirmed these differences are statistically significant. Qualitative analysis with SHAP explainer and LeGrad heatmaps indicated that the ViT localizes pose-specific regions (e.g., lower limbs for walking or running), while simpler feed-forward models often focus on background textures, explaining their errors. These findings emphasize the data efficiency of transformer representations and the importance of explainability techniques in diagnosing class-specific failures.

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MingZe Tang, Madiha Kazi. 2025-06-13. Pose Matters: Evaluating Vision Transformers and CNNs for Human Action Recognition on Small COCO Subsets. https://arxiv.org/abs/2506.11678

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